The dynamic interplay of clan culture and socioeconomic factors on fertility: Evidence from China
Bibliographic record
Abstract
The United Nations has recently identified a critical global population issue characterized by declining fertility rates in many countries. China, as one of the largest populations globally, is undergoing notable demographic changes, transitioning into a period defined by low rates of birth, mortality and growth. These patterns pose significant developmental challenges and require thoroughly examining their underlying causes. Previous research has primarily emphasized economic and social factors influencing fertility, while the cultural aspects–particularly clan culture—remain insufficiently studied. Clan culture is known to affect fertility intentions and gender preferences, but its role in contemporary society warrants further exploration. To address this research gap, we propose a novel varying-coefficient single-index panel data model incorporating latent group structures to assess the relationship between clan culture and birth population across 28 Chinese provinces. Our analysis reveals that clan culture generally fosters fertility, albeit with diminishing trends. We also identify distinct group structures and significant variations in clan culture’s impact on birth population across provinces. Furthermore, we investigate the complex interactions between socioeconomic factors and clan culture on fertility, including the differing effects on the birth of boys and girls. Through advanced computational methods, this study offers valuable insights into the influence of clan culture on fertility in modern China.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".